Method for the management of tasks in a decentralized data network

ABSTRACT

In a method for the management of tasks in a decentralized data network with a plurality of nodes for carrying out the tasks, resources are distributed based on a mapping rule, in particular a hash function. A task that is to be suspended is distributed by dividing the process image of the task into segments and by distributing the segments over the nodes using the mapping rule. Thus, a distributed swap space is created so that tasks can also be carried out on nodes whose swap space is not sufficient on its own. The method can be used in embedded systems with a limited storage capacity and/or in a voltage distribution system, wherein the nodes are, for example, switching units in the voltage distribution system. The method can also be used in any other technical systems such as, for example, a power generation system, an automation system and the like.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a U.S. National Stage Application of InternationalApplication No. PCT/EP2008/068175 filed Dec. 22, 2008, which designatesthe United States of America, and claims priority to DE Application No.10 2008 003 500.9 filed Jan. 8, 2008. The contents of which are herebyincorporated by reference in their entirety.

TECHNICAL FIELD

The invention relates to a method for the management of computingprocesses in a decentralized data network comprising a plurality ofnetwork nodes for executing the processes, wherein resources aredistributed in the data network based on a mapping rule, especially ahash function. In addition the invention relates to a corresponding datanetwork.

BACKGROUND

In technical systems in which technical devices with restricted memoryrequirements are networked via a decentralized data network theparticular problem exists of it not being possible to execute largecomputing processes since the capacity of the individual main memoriesof the technical devices is not sufficient for swapping the processes.There is therefore a requirement for a solution in which, even indecentralized networks containing network nodes with small memorycapacity, a plurality of computing processes and larger processes areable to be executed.

Centralized solutions for executing processes on servers are known fromthe prior art, with a memory being used as swap space which does not lieon the server on which the computing process to be relocated iscurrently being executed.

SUMMARY

According to various embodiments a method for management of computingprocesses for a decentralized data network can be created in which, evenwith restricted memory space on the individual network nodes, executionof a number of computing processes and especially also of largerprocesses is made possible.

According to an embodiment, a method for managing computing processes ina decentralized data network comprising a plurality of network nodes forexecuting the processes, with resources being distributed in the datanetwork based on a mapping rule, especially a hash function, maycomprise: a) One or more computing processes executed on a network nodeand to be suspended in each case is or are stopped and a process imageis created for each stopped computing process; b) The process image of arespective computing process is broken down into slices; c) The slicesof the process image of the respective computing process are distributedwith the aid of the mapping rule to the network nodes, which creates adistributed process image.

According to a further embodiment, the process image in step b;) may beessentially split up into slices of the same size. According to afurther embodiment, the slices distributed in step c) can be stored inrespective main memories in the network nodes, especially in RAMs of thenetwork nodes. According to a further embodiment, the method can be usedin a data network in which each network node is responsible for apre-specified quantity of hash values able to be generated by the hashfunction. According to a further embodiment, the data network may be apeer-to-peer network based on a distributed hash table, in which a rangeof hash values is divided up into hash value intervals and each networkis responsible for a hash value interval. According to a furtherembodiment, in step c) a keyword unique in the decentralized datanetwork can be generated for each slice of the process image of arespective computing process, with the keyword being mapped with thehash function to a hash value and the slice, for which the keyword wasgenerated being stored in the network node which is responsible for thehash value to which the keyword was mapped. According to a furtherembodiment, a keyword for a slice of a process image of the respectivecomputing process can be created from information about the respectivecomputing process and an identification for the slice, especially aslice number. According to a further embodiment, the information aboutthe respective computing process and/or the keyword can be stored in thenetwork node which has executed the relevant computing process before itwas stopped, and/or in which the information about the relevantcomputing process and/or the keyword is managed by a process managementmethod. According to a further embodiment, the information about therespective computing process may comprise a process identification ofthe relevant computing process and/or an identification of the processimage of the respective computing process. According to a furtherembodiment, the process based on a distributed process image may beresumed by a network node intended for the resumption of the process bymeans of the following steps: i) Finding and storing the slices of thedistributed process image distributed to the network nodes with the aidof the mapping rule, especially the hash function, in the network nodeintended for resumption of the process; ii) Combining the slices intothe process image and starting the process based on the process image inthe network node intended for resumption. According to a furtherembodiment, the network node intended for resumption of the process canbe the same network node that has executed the process before it wasstopped. According to a further embodiment, the network node intendedfor resumption of the process can be a different network node from theone that has executed the process before it was stopped. According to afurther embodiment, in step c) a keyword unique in the decentralizeddata network can be generated for each slice of the process image of arespective computing process, with the keyword being mapped with thehash function to a hash value and the slice, for which the keyword wasgenerated being stored in the network node which is responsible for thehash value to which the keyword was mapped, the process based on adistributed process image can be resumed by a network node intended forthe resumption of the process by means of the following steps: i)Finding and storing the slices of the distributed process imagedistributed to the network nodes with the aid of the mapping rule,wherein the mapping rule may include the hash function, in the networknode intended for resumption of the process; ii) Combining the slicesinto the process image and starting the process based on the processimage in the network node intended for resumption, and a respectiveslice of the distributed process image can be found in step i) by thekeyword of the respective slice being mapped with the hash function to ahash value and, based on the hash value, the network node being found onwhich the respective slice is stored. According to a further embodiment,the process based on the process image can be started in step ii) suchthat the assembled process image is loaded into a memory of the networknode intended for resumption of the process and is subsequently executedin the main memory. According to a further embodiment, the method can beused in the data network of a technical system with a plurality oftechnical components, with at least a part of the technical componentsrepresenting a network node of the data network in each case.

According to a further embodiment, the technical system may comprises anenergy distribution network, especially an energy distributionsubstation, with the technical components especially comprisingswitching units in the energy distribution network. According to afurther embodiment, the technical system may comprises an energygenerating system, especially an energy generating system based onturbines. According to a further embodiment, the technical system maycomprise an automation system, especially a production line.

According to another embodiment, a decentralized data network maycomprise a plurality of network nodes for executing computing processes,with resources in the data network being distributed in the network onthe basis of a mapping rule, especially a hash function, and with thedata network being embodied such that the computing processes aremanaged with a method, in which: a) one or more computing process to beexecuted on a network node and to be suspended in each case is or arestopped and a process image is created for each stopped computingprocess; b) the process image of a respective computing process isbroken down into slices; c) the slices of the process image of therespective computing process are distributed with the aid of the mappingrule to the network nodes, which creates a distributed process image.

According to a further embodiment of the decentralized data network, thedata network may be embodied such that a method as described above isable to be executed in the data network.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments are explained in detail below with reference tothe enclosed figures.

The figures show:

FIG. 1 to FIG. 3 schematic views of a peer-to-peer network on the basisof which the distribution of an image to a number of peers isillustrated in accordance with an embodiment; and

FIG. 4 to FIG. 6 schematic views of a peer-to-peer network on the basisof which the combining of a distributed process image for resumption ofthe process is illustrated based on an exemplary embodiment.

DETAILED DESCRIPTION

The method according to various embodiments is used in a decentralizeddata network in which resources are distributed to the individualnetwork nodes with a mapping rule, preferably with a hash function. Hashfunctions are sufficiently known from the prior art, an example of sucha function is SHA1. In particular the mapping of a large data space ontoa compressed smaller data space with fixed bit length is achieved with ahash function. Preferably hash functions are also one-way functions,i.e. the original data element to which the hash function was appliedcannot be derived from the hash value generated from a hash function.Different hash functions can have yet further specific characteristics,in particular hash functions are preferably chaotic, i.e. similar sourceelements of the function lead to completely different hash values,whereby an even distribution of resources in the data network isachieved. The invention is however not restricted to hash functions, andother types of functions for distribution of the resources can also beused if necessary, with these functions preferably having to havesimilar characteristics to hash functions.

In accordance with the method according to various embodiments, in astep a) one or more computing processes, executed on a network node andto be suspended in each case, is stopped and a process image for eachstopped process is created. The process image (usually referred tosimply as the image) represents in this case a memory image of theprocess in the state in which the process was stopped. This image isdivided up in such cases for each stopped task into slices (step b)).The slices are finally distributed in a step c) with the aid of themapping rule, especially the hash function, to the network nodes, sothat a distributed image is created in the data network.

The method according to various embodiments thus also uses the mappingrule generally used in decentralized data networks for the distributionof resources for distributing slices of a stopped task. Consequentlyprocesses can be swapped in a simple manner with existing mechanismsusing distributed swap space, even if the memory space of an individualnetwork node is not sufficient for swapping the individual process.

In an embodiment, the image is essentially split up into slices of thesame size in step b), through which an even distribution of the slicesto the network nodes is made possible.

Step c) of the method according to various embodiments is realized in afurther embodiment by the distributed slices being stored in respectivemain memories in the network nodes, especially in RAMs of the networknodes.

As already shown, the method according to various embodiments ispreferably used in a decentralized data network in which resources aredistributed on the basis of a hash function. In particular in this caseeach network node of the data network is responsible for a pre-specifiedquantity of hash values able to be generated by the hash function.Preferably in this case a peer-to-peer network is used which is based ona distributed hash table. Distributed hash tables are sufficiently knownfrom the prior art. With these tables a range of hash values issubdivided into hash value intervals and each network node isresponsible for one hash value interval.

In a further embodiment, in step c) a unique keyword for each slice ofthe image is generated in the decentralized data network, with thekeyword being mapped with a hash function onto a hash value and theslice from which the keyword was generated being stored in the networknode which is responsible for the hash value for which the keyword wasmapped. The keyword for a slice can in this case be created in any way;preferably the keyword is created from information about the respectiveprocess to which a slice belongs as well as by an identifier for theslice, especially a slice number.

Especially when the process is to be continued at the same network nodeat which it was previously stopped, the information for the respectiveprocess and/or the keyword will be stored in the network node which hasexecuted the respective task before it was stopped. If necessary it isalso possible for the information about the respective task and/or thekeyword to be managed by a process management method, such as roundrobin, priority queue and the like for example. The information aboutthe respective process comprises unique identifiers for identifying thetask, for example a process identifier of the process and/or anidentifier of the slice of the respective process.

The process underlying the process map distributed with the methodaccording to various embodiments is resumed in an embodiment based onthe following steps:

-   i) Finding and storing the slices of the distributed process image    distributed to the network nodes with the aid of the mapping rule,    especially the hash function, in the network node intended for the    resumption of the process;-   ii) Combining the slices into the process image and starting the    process based on the process image in the network node intended for    resumption.

In an embodiment, the process is resumed again in such cases in thenetwork node that has executed the process before it was stopped. Ifnecessary it is however also possible for the network node intended forthe resumption of the process to be a network node other than the onethat has executed the process before it was stopped.

In an embodiment, in which keywords are used for distributing the slicesof the process image, a respective slice of the distributed processimage is found by the keyword of the respective slice being mapped bythe hash function onto a hash value and, based on the hash value, thenetwork node being found on which the respective slice is stored.

The starting of a process based on a process image is undertaken in anembodiment such that the assembled process image is loaded into the mainmemory of the network node intended for resumption of the process andsubsequently executed in the main memory.

The method according to various embodiments is especially suitable foruse in a technical system with a plurality of technical components, withthe at least one part of the technical components in each caserepresenting a network node of the data network. The technicalcomponents thus do not have to be pure computers but can also involvetechnical devices with lower computing power and little memory space, asare used in so-called “embedded systems” for example. Based on themethod according to various embodiments, larger processes can now alsobe executed for technical systems with such devices, since the processesare swapped distributed in the network by including the memory offurther devices.

An example of a technical system in which the method according tovarious embodiments can be used is an energy distribution network,especially an energy distribution substation in which the technicalcomponents especially comprise switching units in the energydistribution network. Likewise the method according to variousembodiments can for example be used in an energy generation system,especially in an energy generation system based on turbines. Furtherareas of application are automation systems such as production lines forexample.

As well as the method described above, various embodiments furtherrelate to a decentralized data network with a plurality of network nodesfor executing the processes, with the decentralized data network beingembodied such that in the network each of the above describedembodiments is able to be executed.

An exemplary embodiment will be described below in relation to FIG. 1 toFIG. 6, based on a peer-to-peer network comprising nine peers or networknodes 1, 2, . . . , 9. The peer-to-peer network is embodied here as alogical ring structure, for example in the form of a chord ring. Thistype of peer-to-peer network is sufficiently known from the prior artand will therefore not be explained in any greater detail here.Resources, typically in the form of files, are distributed in thisnetwork based on a distributed hash table. In such cases a range of hashvalues is subdivided into intervals and each of the peers is responsiblefor an interval of hash values. The resources to be distributed in thenetwork are assigned keywords and these keywords are converted into hashvalues with the aid of a hash function, for example with the functionSHA1. That peer which is responsible for the interval including the hashvalue generated from the keyword then stores the corresponding resource.If necessary resources can also be replicated in the network, i.e.copies of the resources are stored on a number of network nodes. Thismeans that the resources continue to be available even if a peer fails.This replication can also be applied to the distribution of slices of animage described below so that copies of each slice are stored on anumber of network nodes.

The individual peers in the network of FIG. 1 represent individualdevices with a processor for executing computing processes. For examplethe system of FIG. 1 could involve a so-called “embedded system”, inwhich the individual peers represent technical devices of a technicalsystem, for example technical components of a switching unit for a powerdistribution system. Such technical components mostly only have arestricted memory capacity and are therefore only suitable to someextent for executing a plurality of different sizes of program in acontending execution sequence. One of the reasons for this is that suchcomponents mostly have a small main memory and therefore only have theability to a limited extent to swap large processes with the help of themain memory, i.e. move them around or relocate them. To make it possibledespite this to execute large processes on such embedded systems, inaccordance with various embodiments a distributed swap space is createdby a process to be suspended in the program sequence being swapped to aplurality of peers.

FIG. 1 shows a scenario in which preparations are being made forswapping a first process. To this end a memory image or process image ofthe process to be suspended and swapped is created first of all. Thisimage contains different components of the process and especiallyrepresents an image of the main memory of that peer which has previouslyexecuted the process. In FIG. 1 this process image is labeled PI1, andtypically the program code CO, the register settings RS, of the programcounter PC, the status of the heap HE and also the status of the stackST are specified as components of the image. This process image is nowdivided up into a plurality of slices, with the individual slicespreferably being essentially the same size, so that the process image isdivided up by the slices without taking into consideration the content.This means that the individual slices do not necessarily contain entirecomponents of the image, but the components can extend over a number ofslices. In the example of FIG. 1 the process image PI1 has been dividedup into a total of seven slices S11, S12, S13, S14, S15, S16 and S17. Inthis case it can typically occur that two adjacent slices includeelements of the code CO.

FIG. 1 also shows the slices of a further second process, with theslices already having been distributed based on the method according tovarious embodiments to individual peers. The second process has likewisebeen divided up here into seven slices, which are labeled in FIGS. 1 asS21, S22, S23, S24, S25, S26 and S27. Based on the division undertaken,slice S21 has been stored here on peer 1, slice S22 on peer 4, slice S23on peer 7, slice S24 on peer 2, slice S25 on peer 5, slice S26 on peer 8and slice S27 on peer 3.

After the process image PI1 has been split up into the correspondingslices S11 to S17, the slices are allocated in the next step tocorresponding hash values. This step is illustrated in the lower part ofFIG. 2. In this case a corresponding keyword KS11, KS12, . . . , KS17 isinitially defined for each of the slices S11 to S17. This keyword isunique for the respective slice and can for example be composed of anidentifier of the process underlying the process image PI1 and/or anidentifier of the process image itself, with each slice further beingidentified by a specific number. In particular the slices are numberedin this case with numbers in ascending order in accordance with theposition of the slice within the distribution.

Like the generation of hash values from the keywords of resources,corresponding hash values are now created from the keywords KS11 to KS17based on the hash function used in the peer-to-peer network. These hashvalues are labeled in FIGS. 2 as H1 to H7, with the number of therespective hash value showing which of the peers 1 to 9 is responsiblefor this hash value, i.e. which peer contains the interval containingthe corresponding hash value. This means in other words that peer 2 isresponsible for the hash value H2, peer 1 is responsible for the hashvalue H1, peer 3 is responsible for the hash value H3, peer 4 isresponsible for the hash value H4, peer 5 is responsible for the hashvalue H5, peer 6 is responsible for the hash value H6 and peer 7 isresponsible for the hash value H7. In accordance with FIG. 2 the resultof the hash function is that the hash value H2 is created from thekeyword KS11, the hash value H7 from the keyword KS12, the hash value H6from the keyword KS13, the hash value H3 from the keyword KS14, the hashvalue H1 from the keyword KS15, the hash value H4 from the keyword KS16and the hash value H6 from the keyword KS17.

In a next step, which is illustrated in FIG. 3, the slices S11 to S17 ofthe process image PI1 are now distributed to the peers based on the hashvalues generated with the hash function. In this case a slice of theprocess image is stored on that peer which is responsible for the hashvalue created from the slice. As shown by the dashed-line arrows in FIG.3, this means that slice S11 is stored on peer 2, slice S12 on peer 7,slice S13 on peer 5, slice S14 on peer 3, slice S15 on peer 1, slice S16on peer 4 and also slice S17 on peer 6. Thus peers 1, 2, 3, 4, 5 and 7now contain slices both from the first process and also from the secondprocess. By contrast peer 6 contains only one slice from the firstprocess, i.e. slice S17, and peer 8 only one slice from the secondprocess, namely slice S26. The individual slices in this case are storedin the main memories of the peers, with the main memories preferablybeing RAMs. A distributed swap space is therefore created by a pluralityof the main memories on the different peers, so that process swapping isalso possible in peer-to-peer networks in which the individual peers donot have sufficient capacity for swapping a process. The condition mustmerely be fulfilled that sufficient RAM capacity is available in theoverall peer-to-peer network to enable all idle processes to be stored.

It will now be explained on the basis of FIGS. 4 to 6 how the slices ofa swapped process image can be assembled again in order to resume theprocess. This is described on the basis of the swapped second process,of which the process image comprises the slices S21 to S27. In the formof embodiment described below the swap process is accepted again in thiscase by that peer which has also been executing the process previously.In this case it should be noted that the swapping of the second processoccurs in the same way as previously described for the first process.The keywords used for swapping the individual process slices have beenstored in this case in the peer which has previously executed theprocess. If necessary the keywords can however also be generated againfrom corresponding information about the process, especially from theprocess identifier or the identifier of the process image, incombination with a corresponding number of the slice.

As emerges from FIG. 4, the keywords KS21, KS22, KS23, KS24, KS25, KS26and also KS27 for the second process are contained in the correspondingpeer which resumes the process. The keyword KS21 was generated in thiscase based on the slice S21, the keyword KS22 based on the slice S22,the keyword KS23 based on the slice S23, the keyword KS24 based on theslice S24, the keyword KS25 based on the slice S25, the keyword KS26based on the slice S26 and the keyword KS27 based on the slice S27. Thecorresponding hash value is now once again generated with the aid of thehash function used in the peer-to-peer network. In particular the hashvalue H1′ is generated from the keyword KS21, the hash value H4′ isgenerated from the keyword KS22, the hash value H7′ is generated fromthe keyword KS23, the hash value H2′ is generated from the keyword KS24,the hash value H5′ is generated from the keyword KS25, the hash valueH8′ is generated from the keyword KS26 and the hash value H3′ isgenerated from the keyword KS27. The digits in the hash value again showwhich of the peers is responsible for the corresponding hash value inaccordance with the distributed hash table.

As shown in FIG. 5, the corresponding peers which are responsible forthe hash values H1′ to H7′ and which consequently contain thecorresponding slices S21 to S27 are subsequently found with the aid ofthe peer-to-peer protocol used. These slices are then downloaded to thepeer which is to resume the process. The downloading of thecorresponding slices in this case in indicated in FIG. 5 bycorresponding dashed-line arrows. Finally the individual slices S21 toS27 are available in the peer which is to resume the process.

FIG. 6 shows the scenario after the slices S21 to S27 have beendownloaded, which is evident from that fact that the individual peersnow only still contain the slices S11 to S17 of the first process. FIG.6 further shows the concluding step of the method in which theindividual downloaded slices S21 to S27 are now assembled in accordancewith their numbering again into a process image PI2 of the secondprocess. Individual elements of the process image PI2 are againspecified in FIG. 6, namely the code CO', the register setting RS', theprogram counter PC', the status of the heap HE' and the status of thestack ST'. The process image PI2 is finally loaded into the main memoryof the peer intended for resumption of the process, and the execution ofthe process is continued.

As already mentioned above, in the embodiment of the method describedabove that peer is used for executing the process again which hasalready executed the process previously. However this does notabsolutely have to be the case, and if necessary mechanisms can also beprovided according to which another peer resumes the process. Themanagement of the process execution can be taken over here by knownprocess management methods, such as round robin, priority queue and thelike for example.

What is claimed is:
 1. A method for managing computing processes in adecentralized data network comprising a plurality of network nodes forexecuting the processes, with resources being distributed in the datanetwork based on a mapping rule, wherein the mapping rule may include ahash function, the method comprising: a) stopping one or more computingprocesses executed on a network node and to be suspended and creating aprocess image for each stopped computing process; b) breaking down theprocess image of a respective computing process into slices; and c)distributing the slices of the process image of the respective computingprocess with the aid of the mapping rule to the network nodes, whichcreates a distributed process image.
 2. The method according to claim 1,wherein the process image in step b) is split up into slices of the samesize.
 3. The method according to claim 1, wherein the slices distributedin step c) are stored in respective main memories in the network nodes.4. The method according to claim 1, wherein the method is used in a datanetwork in which each network node is responsible for a pre-specifiedquantity of hash values able to be generated by the hash function. 5.The method according to claim 4, wherein the data network is apeer-to-peer network based on a distributed hash table, in which a rangeof hash values is divided up into hash value intervals and each networkis responsible for a hash value interval.
 6. The method according toclaim 4, wherein in step c) a keyword unique in the decentralized datanetwork is generated for each slice of the process image of a respectivecomputing process, with the keyword being mapped with the hash functionto a hash value and the slice, for which the keyword was generated beingstored in the network node which is responsible for the hash value towhich the keyword was mapped.
 7. The method according to claim 6,wherein a keyword for a slice of a process image of the respectivecomputing process is created from information about the respectivecomputing process and an identification for the slice, wherein theidentification can be a slice number.
 8. The method according to claim7, wherein at least one of the information about the respectivecomputing process and the keyword is stored in the network node whichhas executed the relevant computing process before it was stopped,and/or in which at least one of the information about the relevantcomputing process and the keyword is managed by a process managementmethod.
 9. The method according to claim 7, wherein the informationabout the respective computing process comprises at least one of aprocess identification of the relevant computing process and anidentification of the process image of the respective computing process.10. The method according to claim 4, wherein in step c) a keyword uniquein the decentralized data network is generated for each slice of theprocess image of a respective computing process, with the keyword beingmapped with the hash function to a hash value and the slice, for whichthe keyword was generated being stored in the network node which isresponsible for the hash value to which the keyword was mapped, whereinthe process based on a distributed process image is resumed by a networknode intended for the resumption of the process by the following steps:i) finding and storing the slices of the distributed process imagedistributed to the network nodes with the aid of the mapping rule,wherein the mapping rule may include the hash function, in the networknode intended for resumption of the process; and ii) combining theslices into the process image and starting the process based on theprocess image in the network node intended for resumption, and wherein arespective slice of the distributed process image is found in step i) bythe keyword of the respective slice being mapped with the hash functionto a hash value and, based on the hash value, the network node beingfound on which the respective slice is stored.
 11. The method accordingto claim 1, wherein the process based on a distributed process image isresumed by a network node intended for the resumption of the process bythe following steps: i) finding and storing the slices of thedistributed process image distributed to the network nodes with the aidof the mapping rule, wherein the mapping rule may include the hashfunction, in the network node intended for resumption of the process;and ii) combining the slices into the process image and starting theprocess based on the process image in the network node intended forresumption.
 12. The method according to claim 11, wherein the networknode intended for resumption of the process is the same network nodethat has executed the process before it was stopped.
 13. The methodaccording to claim 11, wherein the network node intended for resumptionof the process is a different network node from the one that hasexecuted the process before it was stopped.
 14. The method according toclaim 11, wherein the process based on the process image is started instep ii) such that the assembled process image is loaded into a memoryof the network node intended for resumption of the process and issubsequently executed in the main memory.
 15. The method according toclaim 1, wherein the method is used in the data network of a technicalsystem with a plurality of technical components, with at least a part ofthe technical components representing a network node of the datanetwork, respectively.
 16. The method according to claim 15, wherein thetechnical system comprises an energy distribution network, wherein theenergy distribution network may be an energy distribution substation,with the technical components comprising switching units in the energydistribution network.
 17. The method according to claim 15, wherein thetechnical system comprises an energy generating system, wherein theenergy generating system may be an energy generating system based onturbines.
 18. The method according to claim 15, wherein the technicalsystem comprises an automation system, wherein the automation system maybe a production line.
 19. A decentralized data network embodied in acombination of hardware and software, and comprising a plurality ofnetwork nodes including processors for executing computing processes,with resources in the data network being distributed in the network onthe basis of a mapping rule, and with the data network being configuredsuch that the computing processes causes: a) one or more computingprocess to be executed by a processor on a network node and to besuspended in each case is or are stopped and a process image is createdfor each stopped computing process; b) the process image of a respectivecomputing process is broken down into slices; c) the slices of theprocess image of the respective computing process are distributed withthe aid of the mapping rule to the network nodes, which creates adistributed process image.
 20. A decentralized data network according toclaim 19, wherein the process image is split up into slices of the samesize.
 21. A decentralized data network according to claim 19, whereinthe mapping rule comprises a hash function.